IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v159y2018icp344-360.html
   My bibliography  Save this article

Fault location in power distribution network with presence of distributed generation resources using impedance based method and applying π line model

Author

Listed:
  • Dashti, Rahman
  • Ghasemi, Mohsen
  • Daisy, Mohammad

Abstract

The power distribution networks (PDN) are spread in different street and Alley. Furthermore, nowadays, the DG is used in PDN, especially photovoltaic (PV). Therefore, fault location in these PDNs is complex. In this paper, an improved impedance based method has been proposed for fault location in power distribution network with presence of photovoltaic distributed generation resources. According that the PV has an uncertain behavior in different conditions, the proposed method is designed to be robust against PV behavior and upstream feeder changes. In the suggested method, detail equations are derived to prove a new quadratic equation for locating fault in PDNs using recorded voltage and current at the beginning of feeder and DG terminals. According to this proved equation is depended on to just voltage and currents of source or substation terminals, consequently dynamic modeling of PV and substation is not important in the proposed method. Within this method, the π line model is used for improving the accuracy of the suggested method. To evaluate the accuracy of the proposed method, the modified 11 node test Feeder is simulated in the MATLAB software and sensitivity of the suggested method was investigated against the different fault distances fault types, fault resistances and fault inception angles. Furthermore, the proposed method is investigated and tested on the real test feeder in power system simulator of power system and protection Lab. in Persian Gulf University. The results indicate the high accuracy of the algorithm.

Suggested Citation

  • Dashti, Rahman & Ghasemi, Mohsen & Daisy, Mohammad, 2018. "Fault location in power distribution network with presence of distributed generation resources using impedance based method and applying π line model," Energy, Elsevier, vol. 159(C), pages 344-360.
  • Handle: RePEc:eee:energy:v:159:y:2018:i:c:p:344-360
    DOI: 10.1016/j.energy.2018.06.111
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218311769
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2018.06.111?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Daisy, Mohammad & Dashti, Rahman, 2016. "Single phase fault location in electrical distribution feeder using hybrid method," Energy, Elsevier, vol. 103(C), pages 356-368.
    2. Rusi Chen & Tao Lin & Ruyu Bi & Xialing Xu, 2017. "Novel Strategy for Accurate Locating of Voltage Sag Sources in Smart Distribution Networks with Inverter-Interfaced Distributed Generators," Energies, MDPI, vol. 10(11), pages 1-20, November.
    3. Madeti, Siva Ramakrishna & Singh, S.N., 2017. "Online fault detection and the economic analysis of grid-connected photovoltaic systems," Energy, Elsevier, vol. 134(C), pages 121-135.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ehsan Gord & Rahman Dashti & Mojtaba Najafi & Hamid Reza Shaker, 2019. "Real Fault Section Estimation in Electrical Distribution Networks Based on the Fault Frequency Component Analysis," Energies, MDPI, vol. 12(6), pages 1-29, March.
    2. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi & Nikta Chireh, 2019. "A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine," Energies, MDPI, vol. 12(13), pages 1-18, June.
    3. Saeid Khavari & Rahman Dashti & Hamid Reza Shaker & Athila Santos, 2020. "High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers," Energies, MDPI, vol. 13(9), pages 1-15, May.
    4. Sun, Chenhao & Wang, Xin & Zheng, Yihui, 2020. "An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks," Applied Energy, Elsevier, vol. 258(C).
    5. Mingzhen Li & Jialong Bu & Yupeng Song & Zhongyi Pu & Yuli Wang & Cheng Xie, 2021. "A Novel Fault Location Method for Power Cables Based on an Unsupervised Learning Algorithm," Energies, MDPI, vol. 14(4), pages 1-19, February.
    6. Farshchian, Ghazaleh & Darestani, Soroush Avakh & Hamidi, Naser, 2021. "Developing a decision-making dashboard for power losses attributes of Iran’s electricity distribution network," Energy, Elsevier, vol. 216(C).
    7. Abdul Haleem Medattil Ibrahim & Madhu Sharma & Vetrivel Subramaniam Rajkumar, 2023. "Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network," Energies, MDPI, vol. 16(9), pages 1-42, April.
    8. Bin Yang & Zhanran Xia & Xinyun Gao & Jing Tu & Hao Zhou & Jun Wu & Mingzhen Li, 2022. "Research on the Application of Uncertainty Quantification (UQ) Method in High-Voltage (HV) Cable Fault Location," Energies, MDPI, vol. 15(22), pages 1-15, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saeid Khavari & Rahman Dashti & Hamid Reza Shaker & Athila Santos, 2020. "High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers," Energies, MDPI, vol. 13(9), pages 1-15, May.
    2. D’Adamo, Idiano & Falcone, Pasquale Marcello & Gastaldi, Massimo & Morone, Piergiuseppe, 2020. "The economic viability of photovoltaic systems in public buildings: Evidence from Italy," Energy, Elsevier, vol. 207(C).
    3. Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
    4. Bo-kyu Kwon & Soohee Han & Kwang Y. Lee, 2018. "Robust Estimation and Tracking of Power System Harmonics Using an Optimal Finite Impulse Response Filter," Energies, MDPI, vol. 11(7), pages 1-15, July.
    5. Li, Yuanliang & Ding, Kun & Zhang, Jingwei & Chen, Fudong & Chen, Xiang & Wu, Jiabing, 2019. "A fault diagnosis method for photovoltaic arrays based on fault parameters identification," Renewable Energy, Elsevier, vol. 143(C), pages 52-63.
    6. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.
    7. Tang, Liangyu & Han, Yang & Zalhaf, Amr S. & Zhou, Siyu & Yang, Ping & Wang, Congling & Huang, Tao, 2024. "Resilience enhancement of active distribution networks under extreme disaster scenarios: A comprehensive overview of fault location strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    8. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2023. "Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks," Energy, Elsevier, vol. 266(C).
    9. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
    10. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
    11. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    12. Rafael Cisneros-Magaña & Aurelio Medina & Olimpo Anaya-Lara, 2018. "Time-Domain Voltage Sag State Estimation Based on the Unscented Kalman Filter for Power Systems with Nonlinear Components," Energies, MDPI, vol. 11(6), pages 1-20, June.
    13. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    14. Ramadoss Janarthanan & R. Uma Maheshwari & Prashant Kumar Shukla & Piyush Kumar Shukla & Seyedali Mirjalili & Manoj Kumar, 2021. "Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems," Energies, MDPI, vol. 14(20), pages 1-19, October.
    15. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan & Abdulrahman Alraeesi, 2021. "Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    16. Wu, Lijun & Chen, Zhicong & Long, Chao & Cheng, Shuying & Lin, Peijie & Chen, Yixiang & Chen, Huihuang, 2018. "Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm," Applied Energy, Elsevier, vol. 232(C), pages 36-53.
    17. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.
    18. Jufri, Fauzan Hanif & Oh, Seongmun & Jung, Jaesung, 2019. "Development of Photovoltaic abnormal condition detection system using combined regression and Support Vector Machine," Energy, Elsevier, vol. 176(C), pages 457-467.
    19. Ehsan Gord & Rahman Dashti & Mojtaba Najafi & Hamid Reza Shaker, 2019. "Real Fault Section Estimation in Electrical Distribution Networks Based on the Fault Frequency Component Analysis," Energies, MDPI, vol. 12(6), pages 1-29, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:159:y:2018:i:c:p:344-360. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.